Text document classification based on mixture models
Jana Novovičová; Antonín Malík
Kybernetika (2004)
- Volume: 40, Issue: 3, page [293]-304
- ISSN: 0023-5954
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topNovovičová, Jana, and Malík, Antonín. "Text document classification based on mixture models." Kybernetika 40.3 (2004): [293]-304. <http://eudml.org/doc/33701>.
@article{Novovičová2004,
abstract = {Finite mixture modelling of class-conditional distributions is a standard method in a statistical pattern recognition. This paper, using bag-of-words vector document representation, explores the use of the mixture of multinomial distributions as a model for class-conditional distribution for multiclass text document classification task. Experimental comparison of the proposed model and the standard Bernoulli and multinomial models as well as the model based on mixture of multivariate Bernoulli distributions was performed using Reuters-21578 and Newsgroups data sets. Preliminary experimental results indicate the effectiveness of the proposed model in a text classification problem.},
author = {Novovičová, Jana, Malík, Antonín},
journal = {Kybernetika},
keywords = {text classification; multinomialmixture model; text classification; multinomial mixture model},
language = {eng},
number = {3},
pages = {[293]-304},
publisher = {Institute of Information Theory and Automation AS CR},
title = {Text document classification based on mixture models},
url = {http://eudml.org/doc/33701},
volume = {40},
year = {2004},
}
TY - JOUR
AU - Novovičová, Jana
AU - Malík, Antonín
TI - Text document classification based on mixture models
JO - Kybernetika
PY - 2004
PB - Institute of Information Theory and Automation AS CR
VL - 40
IS - 3
SP - [293]
EP - 304
AB - Finite mixture modelling of class-conditional distributions is a standard method in a statistical pattern recognition. This paper, using bag-of-words vector document representation, explores the use of the mixture of multinomial distributions as a model for class-conditional distribution for multiclass text document classification task. Experimental comparison of the proposed model and the standard Bernoulli and multinomial models as well as the model based on mixture of multivariate Bernoulli distributions was performed using Reuters-21578 and Newsgroups data sets. Preliminary experimental results indicate the effectiveness of the proposed model in a text classification problem.
LA - eng
KW - text classification; multinomialmixture model; text classification; multinomial mixture model
UR - http://eudml.org/doc/33701
ER -
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